1. Executive Summary

The purpose of this test is to evaluate the feasibility of the RV1126 development board as a core processing unit for the second generation of reCamera, focusing on:

- Real-time video analysis capability (YOLOv5/SSD model performance)

- Long-term operational stability (temperature/power consumption)

- Development Environment Maturity (Toolchain Integrity)

Conclusion:

Final Recommendation: RV1126 may not be a good choice for 2nd-gen products(But RV1126B may be suitable), with the following conditions:

1. Usage Constraints:
   • Suitable for single-stream 1080P@30fps detection (7-10FPS YOLOv5s)
   • Not recommended for multi-channel or high-precision scenarios
2. Critical Improvements Required:
   • Enhanced thermal design 
   • Real-time detection performance
3. Outstanding Risks:
   • Stability 
   • OpenCV-Python compatibility with quantized models 

This report evaluates two commercially available RV1126 development boards (large: 10×5.5cm, small: 3.5×3.5cm) through:

• Network bandwidth testing
• Thermal and power measurements under CPU/NPU loads
• RTSP streaming performance

2. Test Results

2.1 Large RV1126 Board Performance

Network Bandwidth Results:

Test Type	Protocol	Bandwidth (Mbps)	Transfer	Packet Loss	Jitter (ms)	Duration	Key Findings	Conclusion
Single-thread TCP	TCP	93.1	222 MB	0%	-	20.03s	Fluctuation (84.4~104Mbps), 1 retransmission	Suboptimal TCP performance (93Mbps)
Multi-thread TCP	TCP	97.6 (total)	233 MB	0%	-	20.01s	Thread imbalance (20.8~30.5Mbps per thread)	Multithreading provides minimal improvement
UDP	UDP	500	596 MB	0%	0.165	10s	Achieves physical network limit	Validates gigabit-capable hardware

Thermal/Power Characteristics:

Scenario	Ext. Temp (°C)	Int. Temp (°C)	Power (W)	Observations
Idle	36	40	0.6~0.75	Baseline measurement
CPU stress test	62	70	1.2~1.4	30°C temperature rise
YOLOv5 inference	73	81	2.5~2.8	Frame rate drops from 7.35 to 6.56 FPS after 10 mins at 80°C thermal equilibrium

2.2 Small RV1126 Board Performance

Scenario	Ext. Temp (°C)	Int. Temp (°C)	Power (W)	Observations
Idle	36	45	0.6~0.7	Higher baseline temperature than large board
CPU stress test	45	52	1.05~1.2	7°C temperature rise
RTSP streaming	73	80	3.0~3.1	1920×1080 @ 2s latency, 15% CPU utilization
SSD detection	72	81	3.0~3.4	Power stabilizes below 3W despite 90% CPU load

Notes:

• Board size: ~10cm²
• RTSP streaming: 73-74°C, 2.65-2.7W power draw

3. Test Methodology

3.1 CPU Load Testing

export TERM=linux

stress --cpu 4 --timeout 60 &

top

3.2 Network Performance

Connectivity Test:

ping 192.168.253.2 -c 10

Bandwidth Measurement:

# Server side:

iperf3 -s -B 192.168.253.1

 

# Client side:

iperf3 -c 192.168.253.1 -t 20 -P 4

3.3 Thermal Monitoring

while true; do

    echo -n "$(date '+%H:%M:%S') ";          

    cat /sys/devices/system/cpu/cpu*/cpufreq/cpuinfo_cur_freq | awk '{printf "%.1f MHz ", $1/1000}';

    cat /sys/class/thermal/thermal_zone0/temp | awk '{printf "%.1f°C", $1/1000}';

    echo "";  

    sleep 1;  

done

3.4 Power Consumption Testing

Testing Methodology :

1.  Power the BV1126 development board using an adjustable power supply

2.  Simultaneously monitor input power

3.  Execute full-load test:

stress --cpu 4 --timeout 180 &  # 4-core full load for 180 seconds

Important Notes :

1.  Temperature sensor paths may vary across devices (common paths include thermal_zone0 through thermal_zone3)

2.  Power testing requires real-time power monitoring capability

3.  Recommend using heat sinks during full-load tests to prevent thermal throttling

4.  Temperature data conversion: Divide raw values by 1000 (e.g., 45000 = 45.0°C)

3.5 NPU+CPU Co-Loading Test (YOLOv5 on Large RV1126 Board)

Implementation Workflow :

1.  Model conversion

2.  Pre-compilation (PC-to-board cross-compilation)

3.  Deployment of quantized algorithm model

Performance Observations :

● Reaches 80°C internal thermal equilibrium after ~3 minutes under dual full load

● After 10 minutes continuous operation:Inference frame rate decreases from 7.35 to 6.56 FPS

● Power consumption stabilizes at 2.5-2.8W

Starting state:

After 3 Minutes :

After 10 Minutes :

Overall

RockChip's official toolchain has good support, and the basic API documentation is complete, but it lacks detailed code files to show demos, and there is still a gap in community support compared with mature manufacturers such as Nvidia and Intel.

Requirement Scenario	Remarks
1080P@30fps Object detection	It can run stably, and the inference frame rate is about 7FPS
Long-term outdoor operation	requires improved heat dissipation scheme (the internal environment can easily exceed 60 degrees)
multi-channel video analysis	Not yet